# AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems

DevFeed: [AirPASS: Over-the-Air Federated Learning via Pinching Antenna Systems](<https://devfeed.tech/articles/airpass-over-the-air-federated-learning-via-pinching-antenna-systems-96858.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2607.06768>)

Author: Seyed Mohammad Azimi-Abarghouyi, Christopher G. Brinton

Published: 2026-10-23T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [Federated Learning](<https://devfeed.tech/topics/federated-learning.md>), [communications](<https://devfeed.tech/topics/communications.md>)

Tags: [access-point](<https://devfeed.tech/tags/access-point.md>), [aggregation](<https://devfeed.tech/tags/aggregation.md>), [antenna](<https://devfeed.tech/tags/antenna.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-it](<https://devfeed.tech/tags/cs-it.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [device](<https://devfeed.tech/tags/device.md>), [federated-learning](<https://devfeed.tech/tags/federated-learning.md>), [framework](<https://devfeed.tech/tags/framework.md>), [homotopy](<https://devfeed.tech/tags/homotopy.md>), [learning](<https://devfeed.tech/tags/learning.md>), [math-it](<https://devfeed.tech/tags/math-it.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [wireless](<https://devfeed.tech/tags/wireless.md>)

## AI overview

AirPASS addresses over-the-air federated learning with a multi-waveguide pinching antenna system at the access point. It aims to select as many devices as possible while keeping aggregation distortion below a set threshold. Because device selection, receive beamforming, and antenna placement are tightly coupled in a nonconvex optimization, AirPASS alternates between two updates: a homotopy-Riemannian margin-consolidation method optimizes device selection and beamforming with the antenna configuration fixed; a homotopy-assisted geometry method updates antenna positions with the selected devices and beamformer fixed. The supplied abstract describes the framework but gives no performance results.

## Source excerpt

arXiv:2607.06768v3 Announce Type: replace-cross Abstract: This paper investigates over-the-air federated learning (AirFL) in wireless systems where the access point is equipped with a multi-waveguide pinching antenna system (PASS). We adopt the widely studied learning-oriented AirFL formulation, which seeks to maximize the number of selected devices while keeping the aggregation distortion below a prescribed threshold. The resulting joint optimization of device selection, receive beamforming, and pinching-antenna placement is highly nonconvex due to the intricate coupling among these system variables. To address this challenge, we develop AirPASS, an alternating optimization framework with two main components: a homotopy-Riemannian margin-consolidation method for device selection and receive beamforming under a fixed PASS configuration, and a homotopy-assisted geometry optimization method for updating the pinching-antenna positions under fixed selected devices and beamformer.